A role for the bacterial GATC methylome in antibiotic

A role for the bacterial GATCmethylome in antibiotic stress survival

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Citation Cohen, Nadia R et al. “A Role for the Bacterial GATC Methylome inAntibiotic Stress Survival.” Nature Genetics 48.5 (2016): 581–586.

As Published http://dx.doi.org/10.1038/ng.3530

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A role for the bacterial GATC methylome in antibiotic stress survival

Nadia R. Cohen1,2,3, Christian A. Ross4,*, Saloni Jain2,5,*, Rebecca S. Shapiro2,6, Arnaud Gutierrez2,6, Peter Belenky7, Hu Li4, and James J. Collins1,2,3,5,6,8,9

1Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, USA

2Institute for Medical Engineering & Science, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

3Howard Hughes Medical Institute

4Department of Molecular Pharmacology and Experimental Therapeutics, Center for Individualized Medicine, Information Technology, Mayo Clinic College of Medicine, Rochester, Minnesota, USA

5Boston University, Boston, Massachusetts, USA

6Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

7Department of Molecular Microbiology and Immunology, Brown University, Providence, Rhode Island, USA

8Harvard-MIT Program in Health Sciences and Technology, Boston, Massachusetts, USA

9Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

Abstract

Antibiotic resistance is an increasingly serious public health threat1. Understanding pathways

allowing bacteria to survive antibiotic stress may unveil new therapeutic targets2–8

. We explore the

role of the bacterial epigenome in antibiotic stress survival using classical genetic tools and single-

molecule real-time sequencing to characterize genomic methylation kinetics. We find that

Escherichia coli survival under antibiotic pressure is severely compromised without adenine

methylation at GATC sites. While the adenine methylome remains stable during drug stress,

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Correspondence: James J. Collins, ; Email: [email protected]*These authors have contributed equally to this study

Competing financial interestsThe authors have no competing financial interests to declare.

Author contributionsN. R. C. conceived of project, designed and performed experiments and wrote the manuscript; C. A. R. performed bioinformatics analyses. S. J. performed experiments; R. S. S. performed experiments and bioinformatics analyses; A. G. contributed intellectually to the project and helped design experiments; P. B. contributed intellectually to the project; H. L. performed bioinformatics analyses and provided mentorship; J. J. C. oversaw the project and provided mentorship.

HHS Public AccessAuthor manuscriptNat Genet. Author manuscript; available in PMC 2016 September 21.

Published in final edited form as:Nat Genet. 2016 May ; 48(5): 581–586. doi:10.1038/ng.3530.

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without GATC methylation, methyl-dependent mismatch repair (MMR) is deleterious, and fueled

by the drug-induced error-prone polymerase PolIV, overwhelms cells with toxic DNA breaks. In

multiple E. coli strains, including pathogenic and drug-resistant clinical isolates, DNA adenine

methyltransferase deficiency potentiates antibiotics from the β-lactam and quinolone classes. This

work indicates that the GATC methylome provides structural support for bacterial survival during

antibiotics stress and suggests targeting bacterial DNA methylation as a viable approach to

enhancing antibiotic activity.

Bacteria exposed to antibiotics mount complex stress responses that promote survival9–14

,

and accumulating evidence suggests that inhibiting such responses potentiates antimicrobial

activity in drug-sensitive, tolerant and resistant organisms2,3,5,8,15–18

. In both prokaryotes

and eukaryotes, genetic pathways underlying responses to environmental insults have been

widely studied and involve some of the most phylogenetically conserved proteins known19

.

In eukaryotes, stress can also elicit epigenetic modification of histones and DNA that

support long-lasting downstream responses20–23

. The role of prokaryotic epigenomes in

stress, however, is much less clear.

Bacteria lack histones, but harbor a diverse group of enzymes able to insert epigenetic

modifications in the form of sequence-specific methylation of DNA bases24

. Prokaryotic

DNA methyltransferases (MTases) function either alone or as part of restriction-modification

systems, participating in various cellular processes including anti-viral defense, cell cycle

regulation, DNA replication and repair, and transcriptional modulation24–26

. While several

methylation-dependent epigenetic switches have been described27–32

, genome-wide

methylation patterns and kinetics have, until recently, been difficult or impossible to study in

a high-throughput manner33–36

. In this study, we use genetic and genomic tools to explore

the function and behavior of the bacterial methylome during antibiotic stress.

To assess the role of DNA methylation in antibiotic stress survival, we first tested the ability

of E. coli lacking different MTases to withstand sub-lethal doses of β-lactam antibiotics.

Laboratory E. coli K12 possesses four functional MTases that methylate adenines or

cytosines within distinct target sequences24,36–40

(Fig. 1a). Survival of sub-inhibitory

ampicillin exposure by log-phase E. coli was unaffected in mutants lacking HsdM, YhdJ or

Dcm MTases. However, bacteria deficient in DNA adenine methyltransferase (Dam) were

highly susceptible to this low drug dose (Fig. 1b and Sup. Fig. 1a,b). Increased ampicillin

susceptibility in dam-deficient E. coli was also reflected in a reduced minimum inhibitory

concentration (MIC) and minimum bactericidal concentration (MBC) (Sup. Fig. 1c).

Complementation with a plasmid expressing dam, but not gfp, restored wild-type survival

levels in Δdam E. coli (Fig. 1c, Sup. Fig. 2a, b). Because Dam might also behave as a

transcriptional repressor independently of its DNA methyltransferase function37

, we tested

the ability of plasmids expressing previously characterized methylation-incompetent Dam

variants38

(Sup. Fig. 2a) to rescue ampicillin hypersensitivity in Δdam E. coli. Consistent

with a role for GATC methylation, mutant Dam expression minimally altered the ampicillin

hypersensitivity of Δdam E. coli, if at all (Sup. Fig. 2b, c). Finally, we sought to determine

whether Δdam E. coli hypersensitivity extended to drugs other than ampicillin. Sub-

inhibitory treatment with aztreonam, meropenem and cephalexin, other β-lactams commonly

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used in the clinic, was also significantly potentiated in the absence of dam (Fig. 1d).

Together, these results suggest that Dam-dependent methylation is important for bacterial

survival during β-lactam stress.

Dam methylates GATC sites throughout the genome of organisms belonging to multiple

orders of γ-proteobacteria, including the clinically relevant genera Escherichia, Salmonella,

Yersinia and Vibrio24

. To explore the behavior of the Dam methylome in the context of

antibiotic pressure, we extracted genomic DNA from E. coli growing in the presence or

absence of ampicillin stress, and analyzed genome-wide GATC methylation over time using

single molecule real-time (SMRT) sequencing. With SMRT technology, epigenetic

modifications on template DNA strands are inferred through the unique kinetic signature

they engender during sequencing35,39

, and the fraction of DNA molecules methylated

(‘frac’) at each GATC site is estimated (Fig. 2a). In all samples, consistent with Dam’s

processive kinetics40

, the majority of GATC sites were detected as methylated in a high

fraction of DNA molecules sequenced (0.97±0.05 on average) (Fig. 2b, Sup. Data Set 1).

Notably, during the log-to-stationary phase transition, we identified 19 GATC sites that

appeared transiently or stably non-methylated, or hemimethylated (Fig. 2c, d; Sup. Table 1,

2; Sup. Data Set 1). Transiently non-methylated sites typically became steadily more or less

methylated over time, following clear temporal patterns (Fig. 2c, d). Because prokaryotes

lack demethylases, non-methylated GATC sequences exist mainly where DNA-binding

proteins sterically hinder Dam activity immediately following DNA replication24

. Consistent

with this notion, 18 of these 19 sites fell within intergenic regions, mostly overlapping with

or closely neighboring footprints of transcription factors (Sup. Table 1). To our knowledge,

only five of these sites have been previously reported as protected24,41–44

.

Remarkably, the GATC methylome and its kinetics were largely unaltered by ampicillin

stress. The genome-wide distribution of frac values was similar in treated and untreated cells

over time, indicating that global methylation levels were not increased or decreased by drug

exposure (Fig. 2b). Furthermore, methylation at the vast majority of GATC sites, including

those displaying dynamic methylation patterns, remained unchanged by treatment (Fig. 2d,

Sup. Table 2, Sup. Data Set 1). Comparison of treated versus untreated samples at each

timepoint revealed only one GATC site (site 19) displaying statistically significant

differential methylation, which occurred at a single timepoint and only on one strand (Sup.

Table 2, Sup Fig. 3). This event’s biological consequence is unclear, however, as expression

of the surrounding gene (gdhA) was unperturbed by ampicillin treatment (data not shown).

Thus, ampicillin stress does not majorly alter the E. coli Dam methylome.

Given the remarkable stability of adenine methylation during antibiotic exposure and the

contrasting drug-sensitive phenotype of dam-deletion mutants, we reasoned that the GATC

methylome must provide structural rather than regulatory support for bacterial survival

during antibiotic stress. Widespread genomic Dam methylation enables cellular processes

requiring discrimination between the fully methylated parental DNA strand, and newly

synthesized DNA whose GATC sites are not yet modified45

. Specifically, transient

hemimethylation at replication forks orients the methyl-dependent mismatch repair (MMR)

system, guiding replacement of mismatched bases to nascent DNA strands only46

.

Importantly, without GATC methylation, the methyl-dependent endonuclease MutH can

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introduce double-strand breaks (DSB) near mismatches targeted for repair47–51

. Mismatches

are rare in log-phase E. coli52

(<1 per replication cycle), but under conditions of stress, their

frequency can increase due in part to induction of the error-prone polymerase IV (PolIV,

encoded by dinB)53–55

. We thus hypothesized that potentiation of β-lactam killing in the

absence of Dam was a result of drug-induced mutagenesis fueling a genotoxic MMR

pathway.

To test this, we assessed the effect of deleting dinB, mutH or the mismatch-binding

component of the MMR complex, mutS, on antibiotic hypersensitivity in Δdam bacteria.

Strikingly, without the mutagenic polymerase PolIV, Δdam E. coli survival of ampicillin

stress returned to wild-type levels (Fig 3a; Sup. Fig. 4a). Similarly, removal of mutS or

mutH on the Δdam background also abrogated ampicillin hypersensitivity (Fig. 3b, Sup. Fig.

4b). In ΔmutH Δdam bacteria, optical density (OD) was somewhat diminished in ampicillin

(Sup. Fig. 4b), but this did not reflect decreased viability during treatment (Fig. 3b). Further

consistent with our hypothesis, we found that Δdam, but not ΔdinB Δdam or ΔmutH Δdam bacteria, developed significantly more DNA damage than wild-type cells during ampicillin

treatment, as assessed by terminal deoxynucleotidyl transferase nick end-labeling

(TUNEL)56

(Fig. 3c, d). Thus, without the GATC methylome, β-lactam-elicited PolIV

introduces mismatches that are converted into lethal DNA strand breaks by a deleterious

MMR system (Fig. 3e).

The finding that genomic GATC methylation supports β-lactam stress survival in E. coli evokes the possibility of targeting Dam to therapeutically potentiate antibiotic drug activity.

Dam is an attractive target as it lacks mammalian homologs but is conserved in several

enteric pathogens57–59

. Furthermore, because muliple drugs can induce mutagenic responses

in bacteria9,12,55,60–62

, treatment with antibiotics other than β-lactams should also be

potentiated in the absence of GATC methylation. Indeed, survival of dam-deficient E. coli in

the presence of sub-inhibitory doses of the quinolones norfloxacin, ofloxacin and

ciprofloxacin was severely compromised compared to wild-type bacteria (Fig. 4a). As seen

with ampicillin, hypersensitivity to ofloxacin could be abrogated by deleting dinB, mutH or

mutS, in Δdam E. coli (Sup. Fig. 5a, b). Consequently, drug potentiation in the absence of

GATC methylation occurs via a similar mechanism across different antibiotic classes, and

may be broadly exploitable.

Next, we sought to determine whether virulent clinical isolates could also be sensitized to

treatment by the removal of Dam. As in E. coli K12, dam deletion in uropathogenic E. coli (UPEC) UTI89

63 significantly increased sensitivity to ciprofloxacin (Fig. 4b). Ciprofloxacin

is a valuable drug for UPEC treatment, but its use is increasingly restricted by the spread of

quinolone resistance64

. To assess whether targeting Dam might allow re-sensitization of

resistant strains, we deleted dam in a highly ciprofloxacin resistant (CiproR) clinical UPEC

isolate bearing multiple common quinolone resistance-conferring mutations (Sup. Table 3).

Remarkably, though dam deletion did not restore full sensitivity to this isolate, the

ciprofloxacin MIC for CiproR UPEC was reduced by over half, and its MBC90 by 4.6 fold

(Fig. 4c). Thus, removing GATC methylation can potentiate antibiotic lethality in both drug-

sensitive and drug-resistant pathogenic organisms.

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Together, our results define an important structural role for the bacterial epigenome in

antibiotic stress survival. Characterization of the adenine methylome revealed highly stable

global GATC methylation levels during log-to-stationary phase transition and sub-inhibitory

β-lactam stress, and while we identified several previously uncharacterized GATC sites with

variable methylation over time, antibiotic stress did not significantly alter these patterns.

Despite the remarkable stability of the GATC methylome, E. coli lacking Dam are

hypersensitive to antibiotic stress. Deletion of E. coli dcm or Neisseria meningitides Mod11A (an adenine Mtase) was also reported to alter bacterial sensitivity to toxic

compounds, but increased resistance, not hypersensitivity, was observed, and attributed to

altered gene expression36,69

. While we cannot exclude additional involvement of

transcriptional dysregulation, our data suggest that the GATC methylome represents an

important backbone structure enabling DNA repair processes to function in the context of β-

lactam and quinlone stress. Specifically, GATC methylation likely supports antibiotic-

elicited mutagenesis dependent on PolIV, an error-prone polymerase induced

transcriptionally or post-translationally in the presence of several antibiotics53–55

. In the

absence of GATC methylation, MMR machinery can convert post-replicative mismatches to

DSBs47

, which accumulate to toxic levels in mutagenizing drug-exposed dam-deficient

bacteria. In Δdam E. coli, the DNA damage response program (SOS) is constitutively sub-

induced65

. dinB is within the SOS regulon66,67

, thus Δdam E. coli may be primed for rapid

PolIV synthesis, enhancing their sensitivity. In addition, DNA breaks caused by MMR in

mutating, drug-exposed Δdam bacteria likely promote SOS pathway induction further,

leading to more PolIV activity68

. Consequently, during antibiotic stress, a toxic feedback

loop may establish itself (Fig. 3e). This model is consistent with earlier observations that

DNA-damaging agents cause MMR-dependent genotoxicity in Δdam bacteria49,50,69–71

;

however, our data further suggest that any initial DNA damage caused by antibiotics directly

is not sufficient kill Δdam bacteria, as the error-prone PolIV is required for hypersensitivity

(Fig. 3a and Sup. Fig. 5a). Measuring mutagenesis rates in Δdam bacteria is challenging

(due to MMR toxicity to mutating cells) and we cannot completely exclude a requirement

for PolIV in introduction of initial DNA damage. This seems unlikely, however, given that

similar levels of damage were recorded in wild-type and ΔdinB bacteria during drug

treatment (Fig. 3d). Thus, our data support a model in which antibiotic stress becomes lethal

as mutagenic PolIV activity fuels a genotoxic MMR response in the absence of GATC

methylation.

Our findings raise the possibility of targeting Dam to enhance the therapeutic activity of

existing drugs. Several classes of antibiotics induce mutagenesis at sub-inhibitory

concentrations9,12,55,60–62

, and may thus be subject to potentiation by this mechanism.

While enhancement of drug activity could be harnessed to lower effective therapeutic doses

in drug-sensitive infections, it may also allow re-sensitization of resistant organisms. Indeed,

our data suggest that targeting Dam methylation can partially reverse ciprofloxacin

resistance in UPEC. More broadly, this observation suggests that mutagenic stress responses

can occur and be therapeutically exploited in highly drug-resistant pathogenic organisms. In

addition to drug potentiation, inhibiting Dam has been proposed as a strategy to weaken

bacterial pathogenicity in vivo25,72–74

, as GATC methylation controls virulence gene

expression in some organisms. While elevated rates of mutagenesis and induction of certain

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prophages75

in the absence of Dam could complicate a Dam inhibitor-based monotherapy,

these drawbacks may be mitigated in the context of combination treatment. In summary, our

results suggest that targeting bacterial epigenomic structures that support mutagenic stress

responses may be a viable strategy to enhancing antibiotic activity.

Online methods

Bacterial strains and plasmids

Laboratory bacterial strains used are derived from E. coli K12 (BW25113 obtained from the

Coli Genetic Stock Center or MG1655 obtained from ATCC). The uropathogenic E. coli strain UTI89 was kindly provided to us from Matt Conover and Scott Hultgren. The

ciprofloxacin-resistant uropathogenic E. coli isolate (UPEC CiproR) was collected from the

Brigham and Women’s Hospital Specimen bank (Sup. Table 3). Deletion mutants on the

BW25113 background were derived from the KEIO collection following KanR cassette

removal. Deletion mutants on the MG1655 background were constructed by allelic

transduction from KEIO collection strains using classical P1 phage transduction, followed

by KanR cassette excision. The dam null phenotype was confirmed by PCR alone or with

electrophoresis of genomic DNA digested with DpnII, which cleaves unmethylated GATC

sites only. For construction of the Δdam UTI89 and Δdam UPEC CipoR strains, the parent

strain bearing a KM208 plasmid-based Red-recombinase system was electroporated with a

PCR amplicon encoding the Δdam::KanR allele. Recovered cells were selected for

kanamycin-resistant homologous recombinants. The plasmid was cured and the KanR

cassette was removed. The genotype of each deletion strain was verified by colony PCR.

The plasmid used in the dam complementation studies, namely pZS*31 (Fig. 1c, Sup. Fig.

2), was obtained from Expressys and belongs to the pZ vector family. pZS*31 has a

pSC101* origin of replication (which yields a low copy number of 3–5 plasmids per cell)

and a chloramphenicol-resistance marker. Genes encoding either Dam (with 500bp upstream

flanking region) or GFP were inserted into the multiple cloning site. For complementation

experiments using mutated versions of dam, the plasmid containing the dam insert was

engineered using either Gibson cloning or site-directed mutagenesis (NEB, Q5 site-directed

mutagenesis kit). Quinolone resistance-conferring mutations in the CipoR UPEC clinical

isolate were identified though whole-genome Illumina sequencing of genomic DNA

(PureLinK Pro-96 Genomic Purification Kit; Life Technologies). Libraries were prepared as

previously described76

. Raw sequencing reads were processed by trimming adapter

sequences and discarding reads shorter then 28bp. Processed reads were aligned to the E. coli MG1655 genome using breseq

77. The genome alignments were searched for known

quinolone resistance-conferring mutations in acrA, acrR, beaS, cpxA, cpxB, envZ, gyrA,

gyrB, marA, marR, mdtA, mdtB, mdtC, ompC, ompF, ompR, parC, parE, soxR, soxS and

tolC genes and their regulatory regions.

Bacterial kill curves, MBC and MIC determination

For timecourse kill curves and MBC assays, stationary-phase bacterial cultures were diluted

at 1:1,000 in 25mLs of LB medium in 250mL baffled flasks. Cultures were grown at 37°C

and 200rpm until they reached an OD of ~0.3. Cultures were transferred to 24-well plates at

500 μl per well, or to 96-well plates at a final volume of 150ul per well, and either left

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untreated or treated with the indicated drugs at specified doses. Plates were sealed using

breathable membranes (BreatheEasy, Cat #: BEM-1) and incubated at 37°C and 900rpm for

the remainder of the experiment. CFUs were enumerated at desired time points (4 hours for

MBC determination) by spot plating 5 μl of ten-fold serially diluted culture onto LB agar

and counting colonies after overnight growth at 37°C. Percent survival at each timepoint was

calculated in relation to the CFU immediately before treatment (0h). For MIC determination,

antibiotics were serially diluted in a 96-well plate and mixed with stationary-phase bacterial

cultures diluted at 1:10,000 in a final volume of 150 μl LB per well. OD was measured from

plates after 24hrs of growth at 900rpm and 37C.

Genomic DNA extraction and PacBio sequencing

Genomic DNA (gDNA) was extracted from E. coli K12 MG1655 LB cultures grown in the

presence or absence of ampicillin using the GenElute Bacterial Genomic DNA Extraction

Kit (Sigma). To assess genomic methylation status, gDNA extracted from stationary-phase

cultures was quantified, digested using DpnII (NEB) and run on an 0.8% agarose gel

containing ethidium bromide. For methylome analyses, samples were sent to UMass

Medical School Deep Sequencing Core, where methylome data were obtained by PacBio

Core Enterprise instrument SMRT. SMRTbell™ DNA template libraries for SMRT

sequencing were prepared according to the instructions described in the ‘Procedure &

Checklist for 10 kb Template Preparation and Sequencing’ document (Pacific Biosciences).

Briefly, genomic DNA samples were first sheared to a target shear size of 10kb using g-Tube

devices (Covaris, Inc.), treated with DNA damage repair mix, end-repaired and ligated to

hairpin adapters. The SMRTbell libraries were prepared using the DNA Template Prep Kit

2.0 (3–10kb) fro Pacific Biosciences. Incompletely formed SMRTbell templates were

digested using Exonuclease III (New England Biolabs) and Exonuclease VII (Affymetrix).

The prepared SMRTbell libraries were sequenced using a 120-min movie acquisition time

and P4 polyerase-C2 DNA sequencing reagent kits following standard instructions for a

PacBio RS II instrument (Pacific Biosciences). Each E. coli sample was sequenced on four

or more SMRT cells yielding a total of approximately 200-fold double-stranded coverage of

the bacterial genome, and two or three biological replicates were sequenced for each

antibiotic treatment condition (Sup. Data Set 2). Sequencing coverage was comparable

between methylated and non-methylated sites (Sup. Table 1, Sup. Data Set 2), ruling out

coverage loss as an explanation for the absence of methylation.

Bioinformatics analyses of SMRT sequencing data

Genome-wide detection of base modification and the affected motifs was performed using

the standard (default) settings in the ‘RS_Modification_and_Motif_Analysis.1’ protocol

included in SMRT Analysis version 2.3.0 Patch 5. The FASTA reference genome sequence

(E. coli K12 MG1655, NCBI NC_000913.2) used for the base modification detection

analyses was obtained from Pacific Biosciences. For motif identification, the base

modification Quality Value (QV) threshold setting was left at the default value of 30.

Interpulse duration (IPD) values were measured for all nucleotide positions in the genome

and compared with expected durations in an in silico kinetic model of the polymerase for

significant associations. ‘Frac’ values were calculated in SMRT Analysis using a standard

mixture model analysis of the pooled kinetic data for a given sample. The frac output value

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provides information about the fraction of individual molecules displaying a methylation

signal at each identified motif site within the genome (Sup. Data Set 1). Methylation frac

values were derived from IPD data within the SMRT pipeline using the single site mixture

model39

. The value 0 was substituted for frac values that were below detection limits. The

values from two or three experimental replicates were compared by Student’s T-test and

FDR adjusted p-values were obtained by the method of Benjamini and Hochberg (Sup. Data

Set 1). Circular graphs were generated using the Circos software package

Flow cytometric assessment of DNA damage

E. coli log-phase cultures were transferred to a 96-well plate (200 μl/well) and treated with

ampicillin (2.5 μg/mL) or hydrogen peroxide (100mM) for 30 minutes to 2 hours at 37°C

and 900rpm. Bacteria were pelleted by centrifugation at 3,000× g for 5 minutes. The

supernatant was discarded. Cell pellets were resuspended vigorously in 200 μl of cold 4%

paraformaldehyde/PBS and incubated at room temperature for 30 minutes to allow fixation.

Bacteria were centrifuged again, then resuspended in 200 μl of cold permeabilization buffer

(0.1%TritonX-100 in 0.1% sodium citrate). After 2 minutes at room temperature, bacteria

were centrifuged and washed in PBS. After pelleting the cells and discarding the

supernatant, cells were resuspended in 50 μl of TUNEL labeling mix (dUTP-FITC and TdT

enzyme) or 50 μl TUNEL labeling reagent (dUTP-FITC) according to manufacturer’s

instructions (Roche; in situ cell death detection kit, fluorescein). Bacteria were stained for 1h

at 37°C. Cells were then washed twice with PBS, resuspended in 1 μg/mL PI/PBS and

analyzed by flow cytometry (BD LSR Fortessa). PI negative cells, which lack genomic

material, were excluded from the analysis. Gating was determined using single color and

unstained controls as references. For Fig. 3d and statistical analysis, background staining

with labeling reagent only was subtracted for each sample to account for treatment

dependent shifts in auto-fluorescence or stain retention.

Statistical analyses

Statistical analysis performed on log10-transformed data (for survival experiments) or on

untransformed data (for TUNEL assay) using a two-way ANOVA followed by a post-hoc t-

test using Sidak’s multiple comparison test correction. In all cases, p-values indicated are

multiplicity adjusted.

Supplementary Material

Refer to Web version on PubMed Central for supplementary material.

Acknowledgments

We thank Tom Ferrante, Ewen Cameron, Kyle Allison, Jonathan Winkler, Jeff Way, Charley Gruber, Prerna Bhargava, Michio Painter, Nawang Sherpa, Tami Lieberman, Laura Certain and Yoshikazu Furuta for critical feedback and technical support over the course of this study. We also thank Matt Conover and Scott Hultgren for generously providing the UTI89 strain, as well as related reagents and protocols. Additionally, we are grateful for SMRT sequencing support from Maria Zapp and Ellie Kittler at the UMassMed Sequencing core, and from Sonny Mark, Khai Luong, Michael Weiand and Onureena Banerjee at Pacific Biosciences. This work was supported by funding from the Defense Threat Reduction Agency grant HDTRA1-15-1-0051, the National Institutes of Health grant 1U54GM114838-01, the Mayo Clinic Center for Individualized Medicine and Donors Cure Foundation, the Howard Hughes Medical Institute, the Banting Postdoctoral Fellowship from the Canadian Institutes of Health and Research, and the Wyss Institute for Biologically Inspired Engineering, Harvard University.

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Figure 1. Increased sensitivity to β-lactams in the absence of dam methylationa. DNA methylation in E. coli K12: methylated DNA bases, methyltransferases (MTases)

and their respective target sequences. b. Wild type (wt) or MTase-deficient E. coli BW25113

were grown in lysogeny broth (LB) to an OD of 0.3, then treated with 2.5 μg/mL of

ampicillin (~0.5 × MIC) or left untreated. c. Log-phase wild-type or dam-deficient E. coli harboring the indicated Cmr plasmid expressing either dam or gfp were cultured in

chloramphenicol (15 μg/mL)-supplemented LB with or without ampicillin (2.5 μg/mL). d. wild-type or Δdam E. coli grown to an OD of 0.3 were treated for 4h with the indicated

drugs. Azt, aztreonam; Mer, meropenem; Ceph, cephalexin. In b–d, survival was determined

by monitoring colony-forming units (CFU) in bacterial cultures at the indicated timepoints,

and is expressed relative to CFU at 0h. Mean percent survival ± SEM of n = 3 independent

experiments is shown; ns, not significant; *, p<0.05, **, p<0.01; ***, p<0.001; ****,

p<0.0001.



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Figure 2. Kinetics of the Dam methylome during normal growth or under antibiotic stressGenomic DNA extracted from wild-type E. coli MG1655 growing with or without

ampicillin (2.5 μg/mL) was analyzed by SMRT sequencing for genome-wide GATC

methylation over 4h. Methylation is shown as the average fraction of sequenced molecules

methylated for each GATC site, or ‘frac;’ dottted red lines indicate the limit of detection

(0.25) a. Representative frac data for untreated bacteria; X-axis, position on selected

genomic segment; arrows non-methylated (black) or hemimethylated (orange) GATC sites.

b. Genome-wide frac distributions during growth in LB (solid line, gray fill) or ampicillin



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(dashed line, no fill) over time; mean frac ± SD are shown. c. Genome-wide kinetics of

adenine methylation at GATC sites during log-to-stationary phase growth in LB; black lines

indicate frac values as shown in (a); colored hashes show the position of genes either

strands; the innermost ring is a reference map of genomic positions in megabases; oriC,

origin of replication; colored indicators on the outer most ring highlight sites detected as

non-methylated (frac<0.025, coefficient of variation<0.5) in at least one sample set, with

colors corresponding to methylation increase (red) or decrease (blue) over time, stable non-

methylation (black), hemimethylation (orange) or other (gray). d. Methylation kinetics for

untreated (solid line) and ampicillin-treated (dotted line) E. coli at GATC positions that are

non-methylated in at least one sample; x axis, time; mean frac ± SEM of n = 2–3

independent experiments is shown.



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Figure 3. PolIV-dependent mutagenesis fuels MMR-mediated DNA damage in β-lactam-stressedΔdam E. colia–b. The indicated E. coli BW25113 strains were grown in LB to an OD of 0.3, then treated

with ampicillin (2.5 μg/mL) or left untreated. CFU in bacterial cultures were monitored

hourly to assess survival. Mean percent survival ± SEM of n = 2 independent experiments is

shown. c. Log-phase E. coli grown in LB alone, with hydrogen peroxide (100 mM) or with

ampicillin (2.5 μg/mL) for the indicated time were assayed for DNA breaks by terminal

deoxynucleotidyl transferase (TdT) nick end labeling (TUNEL). The fluorescence

distribution of each sample incubated with fluorescent label in the presence (solid line) or

absence (shaded histogram) of TdT is displayed. A representative experiment is shown;



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MFI, mean fluorescence intensity. d. DNA damage as assessed as in (c) at 1hr. Mean percent

DNA damage positive ± SEM of n = 3 independent experiments is shown; statistical

comparisons between each mutant strain and wild-type were not significant unless otherwise

indicated; ****, p<0.0001. e. Schematic model of antibiotic potentiation in the absence of

Dam methylation.



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Figure 4. Quinolone toxicity is potentiated in laboratory and pathogenic Δdam E. colia. wild-type or Δdam E. coli BW25113 grown to an OD of 0.3 were treated for 3h with or

without the indicated drugs. b. Log-phase UTI89 uropathogenic E. coli (UPEC) or Δdam UTI89 was treated with 15ng/mL of ciprofloxacin or left untreated. Mean percent survival

(compared to t = 0h) ± SEM of n = 2 independent experiments is shown. c. Determination of

ciprofloxacin MIC, left panel, and MBC90, right panel, for CiproR UPEC by broth

microdilution in LB. wild-type and Δdam MIC are 133 μg/mL and 59 μg/mL, respectively;

wild-type and Δdam MBC90 are 316 μg/mL and 68 μg/mL, respectively. Dotted lines

indicate cut-off values for MIC (OD<0.1) or MBC90 (10% survival), MBC90 values were

interpolated using a sigmoidal curve fit model as shown. In a–c, means ± SEM of n = 2–3

independent experiments is shown; ns, not significant; ****, p<0.0001.



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A role for the bacterial GATC methylome in antibiotic